Fluid flow detection and analysis device and system

ABSTRACT

A fluid monitoring system has a plurality of sensors, the sensors being acoustically coupled to a pipe system containing interconnected fluid-transporting pipes. The sensors collect sensor data that is analyzed by an analysis system to provide leak identification on the pipe system. Each sensor in the plurality of sensors is configured to be programmed adaptively by the analysis system to improve system performance parameters. A method for analyzing fluid flow is also described in which data from a plurality of acoustic sensing devices is analyzed using learning algorithms.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/605,657, entitled “Fluid Flow Detection and Analysis Device andSystem,” filed on May 25, 2017, and issued as U.S. Pat. No.10,732,069which is a continuation of U.S. patent application Ser. No.14/097,222, entitled “Fluid Flow Detection and Analysis Device andSystem,” filed on Dec. 4, 2013 and issued as U.S. Pat. No. 9,664,589;which claims priority to U.S. Provisional Patent Application No.61/733,207, entitled “Fluid Detection and Analysis” and filed on Dec. 4,2012, all of which are hereby incorporated by reference for allpurposes.

BACKGROUND OF THE INVENTION

Fluid flow and leak detection systems for plumbing systems provide avaluable function by protecting the surrounding environment (indoor oroutdoor) from damage due to fluid leakage. Such damage can far exceedthe cost of the leaked fluid, and the combined annual cost of buildingdamage from leaking plumbing systems exceeds $1B in the United States.Leaks in outdoor plumbing systems are also a pervasive problem, withindustry analysts estimating that 30% of treated potable water is lostto leaks.

Despite the pervasive nature and high consequential cost of plumbingleaks, products capable of detecting leaks that have been on the marketfor many years have seen limited market penetration.

Systems designed for identifying leaks in indoor plumbing systems arecurrently either too costly, too difficult to install or provide limitedprotection. The dominant approach for such systems relies on moisturesensors that are activated by contact with pooling water under or nearthe leak.

It is common to see apparatuses that either employ a float-switch or anelectronic moisture sensor as the detection apparatus. Theseapparatuses, an example given by the Leak Alert water detector byZircon, resemble a puck or small box which is placed in an area wherefluid pooling is expected upon the failure of a local pipe system. Anexample would be at the lowest point of the floor under the watercircuit of an air conditioning system. Another example of this categoryemploys the use of Time Domain Reflectometry (TDR) or impedance changesto detect moisture contacting a wire or mesh of wires. Theseapparatuses, an example being the ProH2O system by Safe Fire Detection,Inc, have a small sensing unit connected to a length of detection cableor wire, which is strung in an area expected to become wet upon thefailure of a local pipe system.

Using apparatuses of this category requires precise knowledge of whereleaked fluid will flow and requires a sufficient volume—sometimessignificant—of leaked fluid to pool at the apparatus in order for a leakto be detected. Additionally, because they detect fluid only locally, alarge number of such apparatuses are needed for adequate surveillance ofa significant pipe network, for example that found in a residence orcommercial building.

Leaks in outdoor plumbing systems are generally detected using highlytrained professionals with sophisticated and costly portable acousticmonitoring equipment. These units are affixed to elements in theplumbing network, such as fire hydrants, and the equipment picks upsounds that trained personnel can recognize as originating fromunderground leaks. While proven effective, this labor-intensive approachproves too costly for continuous or widespread use. Furthermore, thespecialized nature of the identification limits the scalability of thisapproach.

Another approach to leak detection utilizes highly accurate flow metersthat enable system operators to identify leaks through mass-balancecalculations. Sensors used in these systems require high accuracy, andas a result are focused only on the flow conditions in the pipe to whichthey are attached. Additionally, their high accuracy usually comes withmoderate to high power requirements, high cost and sometimes difficultyof installation. The high power requirement can be especiallyproblematic because it either precludes battery operation, or theproduct has a short lifetime between battery replacements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a cutaway perspective view of one embodiment of an acousticsensor device;

FIG. 2 is an exemplary detailed schematic of the sensor device of FIG. 1;

FIG. 3 is a schematic of an exemplary communications network for usewith the device of FIG. 1 ; and

FIG. 4 is a flowchart of operation of a sensor in an acoustic sensingsystem, in one embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Flow detection systems for monitoring water systems, such as in aresidential home, can be costly and difficult to install. In the presentdisclosure, embodiments of a sensing element, an acoustic capturesystem, and a data analysis system for use in fluid monitoring shall bedescribed. The systems are capable of detecting leaks in plumbingsystems, even up to a large scale, with reduced cost and ease of use.

Acoustic Capture and Analysis of Signals from Fluid in a Pipe

There is a long-felt need for flow sensors that are simple, low-cost,non-invasive, and easy to install. Most conventional flow meters requirephysical contact with the fluid, which typically requires installationby a professional plumber. Non-contact (or non-invasive) flow sensorshave been commercialized using acoustic sensors. Existing acoustic flowmeasurement devices use active techniques, either with Doppler analysisor by differential time-of-flight analysis. These approaches require acombination of acoustic transducers and one or more acoustic sensors.Currently, to thoroughly maintain surveillance on the integrity of thewater system in a house or building, a plurality of these flow sensordevices are needed, at least one per pipe branch being observed;however, the expense of multiple flow sensors can be cost-prohibitive.Current techniques are focused on supplying accurate flow rateinformation on the local pipe, and therefore use more complex, possiblymechanically intrusive, expensive techniques. Easy-to-install andoperate flow sensors are uncommon. Most are not battery-powered and alsodo not have simple means to attach to a communication link. Theinstallation process of current sensors includes physically attachingsensor(s), connecting sensor(s) to a power supply and supplying acommunication link. A battery powered (cordless) solution would have astrong advantage in this regard, as electric power outlets are notalways near the desired sensor mounting locations. To maximize theservice life of such a device, great care needs to be paid to the use ofelectrical power.

Another characteristic common to the existing commercially availableflow sensors is that they are designed to work in isolation. Although aflow sensor may be specifically designed for use in a system with otherflow sensors, each sensor operates as an independent device, designed toaccurately determine the flow rate only of the pipe to which it isattached. Gathering information from multiple sensors about a complexpiping system is not possible with currently available systems.

In the present disclosure, a flow monitoring system has a cost andconnectivity that enables deployment of a small fleet of sensors atevery site, while maintaining sufficient accuracy for the user. Theindividual sensor units used in this system are non-invasive and batterypowered, with extended operating life on a single battery charge. Thesensor units are configured to be programmed adaptively to improvesystem performance parameters. These parameters may include batterylife, acoustic sensitivity, leak localization and false alarm rate. Theadaptive programming of the fluid flow monitoring system may beperformed by an analysis system housed with at least one centralprocessing unit.

FIG. 1 provides a cutaway view of an embodiment of a representativesensor device 100, which is a small electronics board and a sensor thatis acoustically coupled to a pipe and enclosed within an inexpensivehousing. Pipe 101 is the pipe being monitored. The flow sensor device100 includes a device housing 102, which may be made of a conformalmaterial, and a strap 103 with a clamp for mounting. The strap 103 isintegral to the housing 102 for affixing the device to the pipe 101.Cutaway 104 exposes instrument package 105, which is further describedin the detailed view 110. In this embodiment, instrument package 105includes an acoustic sensor 106, a microcontroller 107 for the acousticsensor 106, battery 108 to power the instrument package 105, and aprinted circuit board 109 or other mounting means for the instrumentpackage 105. In this embodiment, the microcontroller 107 includesintegrated wireless communication and an analog to digital converter,although other configurations are possible. The battery 108 may or maynot be rechargeable. The package 105 may or may not allow for batteryreplacement. The design with the lowest maintenance burden on the enduser will have a long lifetime (e.g. 3-5 years) from a single batterycharge. The device 100 acoustically couples the sensor 106, such as anaccelerometer or a microphone, to the wall of a pipe carrying a fluid.The microphone may be, for example, an intimately-connected piece ofpiezoelectric material, or an intimately-connectedmicroelectromechanical system (MEMS) device. The sensor 106 may beoff-the-shelf or custom made. The device 100 records the acoustic signalcaused by running fluid and associated fixtures (sinks, toilets,showers, dishwashers, sprinklers etc.). It will also record theacoustics when there is no fluid running, as a baseline. In oneembodiment, baseline values may be based on taking samples at times ofhigh probability of no fluid flow, for example at late night hours.

Extended battery life, for example 2 years or longer, is desired, yetbattery capacity is constrained by size and cost considerations. Coincell batteries are one low cost, compact option. These batteriestypically operate at 1.8V to 3V and have limited capacity. For example acommon battery is the Panasonic CR2025, with 3V output and 165 mAhcapacity. Many other battery options are available, but size and costconsiderations favor batteries with relatively small capacity (e.g.,<1000 mAh). The desired battery life must be obtained through selectionof appropriate low power components, notably the microphone, amplifier,analog to digital converter, microcontroller, and communication chips.

Microphones and accelerometers that are designed for many applicationswithin consumer products, such as mobile phones, are particularlyappropriate for use in the present acoustic sensing devices due to theirlow power consumption and compact size (e.g., <10 mm length and width,<5 mm thickness). An example is the Analog Devices model ADXL335accelerometer, which is 4 mm×4 mm×1.45 mm, includes a built-inamplifier, can operate at voltages down to 1.8V and has typical currentconsumption of 300 microamps. However, even this low power consumptiondevice requires additional system optimization to achieve a long batterylife. If the accelerometer were continuously monitored at 300 microamps,the entire battery capacity would be used within 21 days. This does notyet include the power required to store and transmit the data.

Similarly, the analog to digital conversion and microprocessing elementsare constrained by power considerations. The analog to digital converter(ADC) may be a discrete component or may be included in themicrocontroller or in the acoustic sensing device. In either case, thecurrent consumption for the ADC will typically be 30 microamps or more,for sampling rates in the acoustic range (e.g., 400 to 10,000samples/second). Through specialized programming of the present methods,the microcontroller must limit the active time for microphone oraccelerometer sensor readings in order to extend the battery life, andmust itself be capable of operating with significantly reduced powerbetween active states. An example of such a microcontroller is the TexasInstruments MSP430 family. These microcontrollers include severaldifferent operating modes with successively reduced power andfunctionality. For example, a standby mode with typical currentconsumption of 1 microamp is available.

Compact sensor size is desirable for many applications such asresidential and commercial building use. Typical pipe diameters are inthe range of 12 mm to 25 mm, and a sensor of similar dimensions isdesirable. Selection of a compact microphone or an accelerometer sensorand battery are typically the most difficult, as small microcontrollersand wireless communication devices are widely available. Coin cellbatteries are available with diameters of 25 mm and less. Lithiumpolymer batteries are also available with footprints below 25 mm andthickness below 5 mm. High sensitivity microphones or accelerometers(e.g., accelerometer sensitivity >4000 mV/g, or microphonesensitivity >−30dBV/Pa) are typically not available in such smalldimensions. Compact accelerometers are available, such as the ADXL335mentioned previously, but sensitivity is typically 1000 mV/g or less. Byusing the microcontrollers mentioned above, that enable very low averagepower consumption, these compact devices and batteries can be selected.

FIG. 2 is a schematic of an exemplary sensing device 200. Sensing device200 includes acoustic sensor 210, battery 220, analog-to-digitalconvertor 230, microcontroller 240, and communication device 250.Microcontroller 240 provides local control to the individual sensingdevice 200. Microcontroller 240 may or may not include integratedanalog-to-digital convertor 230 or integrated communication device 250.Acoustic sensor 210 may be, for example, a microphone or piezoelectriccomponent as described above. The sensor 210 may be, for example, anaccelerometer with sensitivity below 4000 mV/g, or a microphone withsensitivity less than −30 dBV/Pa. Battery 220 is a low power, compacttype as described above, such as having a capacity less than 1000 mAh.The battery 220 provides power to the microcontroller 240 eitherdirectly or through a power conditioning circuit. The acoustic sensor210, analog-to-digital convertor 230 and communication device 250similarly receive power from the battery 220 either directly, throughpower conditioning circuits or from the microcontroller 240. Themicrocontroller 240 reads the acoustic signal from the acoustic sensor210 either directly or indirectly. Direct measurement can be eitheranalog, read into an analog-to-digital converter integrated into themicrocontroller 240, or digital, read from an analog-to-digitalconverter integrated into the sensor. Indirect measurement can be eitheranalog, where the signal from the acoustic sensor 210 is first passedthrough analog signal conditioning circuits, or digital, where thesignal from the acoustic sensor 210 is passed through an externalanalog-to-digital converter. The microcontroller 240 sends and receivesinformation via the communications device 250.

The sensor of the present disclosure is designed to be installed ontothe pipe without the use of tools, such that it is efficiently attachedto and detached from the pipe. To this purpose the housing (e.g. housing102 of FIG. 1 ) may be constructed of conformal materials, for example,a low durometer rubber or plastic. The conformal material accommodates avariety of pipe sizes while maintaining acoustic contact. There may bean integral strap or clamp that will be used to affix the device.Alternately, the device may be bonded to the pipe with, for example, anadhesive, such as a pressure sensitive adhesive. The operation ofaffixing the device may power it up for the first time. This can be doneby pulling off a tab on the device's sensing surface, by having thestrap tensioning cause a switch to close, or by many other techniquesknown in the art. In some embodiments the assembly may contain acombination battery/hardware door/connection strap. In other embodimentsthe assembly may be a single, fully encapsulated device.

After it is affixed, the device will determine its health, then searchfor the wireless network which, via a base station, connects to anavailable communications network, most commonly based on Wi-Fi. To aidin installation, an application running on a portable device willregister when the device attaches and will report the health.

This sensor device is a part of an inexpensive, robust, easily deployedsystem to protect buildings against damage from fluid leaks. It allowsestimation of flow rates in a system of pipes without intrusion into thepipe, and without the use of high power components. It providesinformation needed to estimate flow and protect the building.Additionally, the sensor provides transient information on the change offlow rates within a system of pipes containing interconnectedfluid-transporting pipes.

The device may or may not reduce the data set by running algorithmsdesigned to reject non-significant data. This algorithm may, forexample, be a bandpass filter or multiple bandpass filters that rejectacoustic frequencies that do not give information on flow rate or giveredundant information. Local processing will be performed when itresults in improved sensor accuracy or sensitivity, reduced energy usage(and longer battery life) or in enhanced communications signal strength.

FIG. 3 illustrates an exemplary communications network 300 in which afirst sensor device 301 is mounted on a pipe, and a second sensor device302 is mounted on another pipe within the same plumbing system. Forcomprehensive monitoring of the plumbing system, the devices may bepositioned throughout the system such that their regions of coverageoverlap, though this is not required for useful services to be rendered.Programmability of sensors in the present disclosure and learningalgorithms of the analysis system in the present disclosure allow wideflexibility in spacing between sensors, simplifying the installationprocess. For example, sensor to sensor spacing may be 2m in one part ofthe system and 6m in another part of the system. Once the system haslearned the characteristic signature—for example, the relativeamplitudes and frequency spectra at each sensor—of a fluid use event(e.g. toilet flush), the individual sensors can be programmed to improveresponse to future events, for example with a different wake-upthreshold. Other components of the system may include base station 303(optional) with root node of the mesh network, wireless local areanetwork (e.g, Wi-Fi) router 304, and internet connection 305. In otherembodiments, connections may be hard-wired or made via cell-phone.Arrows 306 and 307 indicate a wireless mesh connection,self-configuring, for example conforming to the Zigbee standard. Otherwireless communication architectures are also possible, such as star,and point to multi-point. Arrow 308 indicates a Wi-Fi connection, forexample, conforming to the 802.11 standard. This network isbidirectional, so the device can be commanded from the base station to,for example, take a set of samples, accept new algorithms, acceptupgraded software, supply raw, unprocessed data, provide batterycondition, device health information etc. The functions of base station303, in some embodiments, may instead be provided by software in thebuilding that is being serviced, or in a cloud (remote) network, orlocated elsewhere. Other computer and peripheral hardware other thanthose explicitly described here are also possible.

The present system utilizes the advent of low cost sensors, wirelessconnectivity, and highly integrated, powerful, inexpensive processors inreducing cost. Much of the signal processing can be committed tosoftware run at remote locations. The sensors and processors themselves,targeted at the smartphone market amongst others, are very affordable.The device has a simple attachment means that will not require tools,thus facilitating easy installation. Additionally, the device has aself-contained power supply that can last for several years, and it canautomatically attach itself to a wireless communications system withoutany user interaction.

Service life is maximized through the use of relatively new, low powersensors, processors and wireless communications hardware, many of whichhave been developed for remote, low maintenance applications. Thesedevices typically have very low duty cycles, meaning that they remaindormant longer than they are actively collecting data, processing orcommunicating the data. For example, a given sensor device may wake uponce each 10 seconds and collect data for only 100 milliseconds,corresponding to a duty cycle of 1%. This greatly reduces the averagepower consumption relative to a device that is on most of the time. Theprocessor may be configured to perform data reduction, and to transmitonly appropriate information. It may adaptively operate the sensors tominimize data loss and reduce power consumption. Computer and peripheralhardware in the system may, for example, be used to minimize use ofbattery by learning to estimate the probability of an acoustic signal'spresence over time, to compress data and enable low bandwidthtransmission, to prioritize data, to synchronize with a master clock,and/or to transmit acoustic data synchronized to the master clock. Theduty cycle may, for example, be much lower during periods of expectedinactivity in the plumbing system. Key data analysis and decisions maybe made by a centralized processor.

Data Analysis System

A data analysis system to monitor the integrity of a piping system andattached equipment in a building or vessel shall now be described.Currently, to understand fluid flow within the branches of a pipesystem, individual flow sensors need to be mounted along each branch.Limitations of these current techniques include the need for multiplesensors, and the inability to determine any problem other than as itaffects flow measurement. That is, acoustic changes in the pipe networkitself will not be registered. Furthermore, although these systemsprovide accurate flow rate readings, for some applications absoluteaccuracy may not be as critical.

In the present methodology, data from multiple sensors in a pipe systemis utilized to learn about fluid flow rate and possible problems withflow. In some embodiments, the analysis system takes time-dependent datafrom multiple sensors of various types, e.g. flow rate, temperature,pressure, and acoustic signature. Analysis of multiple data streams maybuild confidence in decisions about the integrity of the system undersurveillance. The sensors do not necessarily need to be mounted on everypipe branch, since the pipes work as acoustic waveguides, and ingeneral, a unique combination of acoustic signatures from each sensorwill be generated for each different flow combination. Analyzing thesesignatures should be sufficient to determine the branch(es) with flowingwater. These signatures often will include the relative time of theonset of the signal reception, for each different signal as well as therelative amplitudes of the signal at each sensor that detects a signal.

The signatures for fluid flowing in a pipe include: (a) The mechanical“ringing” of each section of pipe when flow is initiated, turned off, orwhen an amount of entrained air passes through. This ringing can be usedto further firm up the identity of the pipe carrying water; (b)Vibrations induced by non-laminar fluid flow within the pipes; (c)Vibrations caused by leaks, where cavitation and other turbulentbehavior causes higher frequency noise; and (d) Long-period signalscaused by very slow leaks resulting in droplet formation and release.There are also other reasons for an acoustic signature being generatedby fluid flowing in a pipe, as would be known by one versed in the art.

Much quantitative information may be added to the above analysistechnique by combining external data from an accurate flow ratemeasurement device at the inlet end of the pipe system to the data setfrom the multiple sensors to form a set of system data. More flow ratemeasurement devices will ease the determination, but are not strictlynecessary. The process of estimating flow rates to a greater accuracyinvolves a learning algorithm that recognizes unique combinations ofacoustic signatures from the set of sensors, correlates this with a flowrate measured by the accurate measurement device(s), and continuallyrefines the correlation activity. Optionally, the process may run alearning program that steps the owner through a process of firstrecognition of the various devices on the pipe system. This program willask the user to identify operating devices in an order designed tomaximize the learning process. This process can be convenientlyimplemented on a mobile device or other portable computing system, suchas a tablet or laptop computer.

Once a reasonable determination of combinations of signatures and flowshas been determined, with or without external data from an accurate flowrate measurement device, anomalous signature events can be analyzed togive an estimate of a flow problem—for example, a broken or stoppedpipe, or even a pipe that has come loose from the wall. The process ofconverting acoustic signals to flow information may be verycomputationally intensive, and so should be carried out where computingcapacity and electrical power are inexpensive, for example in the cloud.

Embodiments of the methodology may include adaptive analysis over timethat allows for the detection of very small leaks and reduces powerconsumption by tailoring sensor and processing usage (duty cycle).Analysis may include pattern matches with stored signatures, where thesignatures may include, for example, temporal and weather information,filtered spectral information, and signatures that are “learned” viauser input (e.g. toilet was flushed). Analysis may include using data onpiping age and type, and/or equipment age and type. Adaptive analysisover time allows the system to warn about maintenance needs of attachedequipment. Thus, the data system learns the characteristic signatures ofa piping system and attached equipment in a building. Furthermore, thedata system may include external data, which may include elements ofweather measurements and forecasts, information about the pipe system,and information from a flow meter attached to the pipe system.

System sensitivity may be improved over individual sensor sensitivity byusing the combined signals from a plurality of sensors. For example, thecentral processor may identify a specific acoustic frequency range orranges active during a specific fluid flow event based on data from oneof the sensors, for example, the one closest to the appliance in use orclosest to the leak location. The processor can adjust other sensors tofocus on a subset of active acoustic frequencies which will reduce thesignal to noise ratio for each sensor. The combination of these signalsprovides more complete information to the central processor andtherefore better sensitivity and accuracy than one individual sensor.Thus, a processing component that is in communication with a pluralityof sensing devices is capable of performing analysis on data capturedfrom the sensing devices, where a system leak detection sensitivityand/or accuracy analyzed by the processing component is higher than aleak detection sensitivity and/or accuracy of the sensing devices.

Programmable sensors can therefore be used to further improve systemperformance. In some embodiments, the sensors may be programmable toimprove the sensitivity of the detection of leaks; that is, leakidentification in a pipe system. For example, leaks produce acousticsignals with different profiles than conventional fluid use, oftenincluding higher frequency components. In other embodiments, the sensorsmay be battery powered, and may be programmable to increase theoperating time of the battery. For example, the duty cycle of a sensormay be reduced during extended periods of low fluid flow, such asnighttime or when the building is unoccupied. As another example, thepower of the wireless transmitter may be reduced if the associatedreceiver detects sufficient signal strength. The programmability mayinclude the sensors being at least partially directed by instructionsfrom a central processing unit. In yet other embodiments, theprogrammability includes selecting certain time periods to record andprocess signals, and certain time periods to adopt a mode of low powerdormancy. For example, the time periods may include selecting times ofhigh fluid flow and times of negligible fluid flow. Leak localizationmay be improved by, for example, programming the frequency ranges forsensors proximal to the fluid leak, as determined by the strength oftheir overall acoustic signals. The amplitude of signals at specificfrequencies—such as, frequencies that the system associates with theleak—from proximal sensors can be compared. Because acoustic waves atdifferent frequencies attenuate at different rates along the length of apipe, the location of the leak relative to proximal sensors can beestimated.

False alarm rates can be improved by programming sensors with differentalarm thresholds. As the system is notified of false alarms, thethresholds for individual sensors can be adjusted. For example, sensorsclose to appliances generating strong acoustic noise (e.g. washingmachines) can have increased alarm thresholds.

The analysis methodology may utilize computing resources remote from abuilding or buildings with sensors on the network. The methodologyincludes software code that embodies the various ideas enumerated above.The methodology includes communications with the sensors (which maychange location, number or type over time) according to a schedule, orwhich may also be event driven. For example, the process may query thesensor array more often in a rain storm local to the buildings ofinterest. The methodology can maintain the sensors, by ensuring theyhave the latest software, and making enquiries of their battery stateand other observable health characteristics. According to a schedule,the methodology can communicate to parties the building or sensornetwork owners designate, with a report indicating health of the sensorsystem and the pipe system, and optionally fluid usage statistics. Themethodology will communicate with designated parties anytime there is asuspected problem. The action will depend on the severity of theproblem. It can also command one or more optional valves to close andshut down the fluid supply. The methodology enables inexpensive, robust,easily deployed protection for buildings against damage from leakedfluid.

In one embodiment, management software running on a centralizedprocessing unit is in communication with a plurality of sensors. Inother embodiments, management software may run on a distributed set ofprocessing units. This distribution could be, but is not limited to: 1)processing units running identical code in different geographic areas,each with a local set of sensors to supervise; or 2) distributed coderunning on the sensors, a base station in each house and a centralprocessing unit or units, the combination of all capable of carrying outthe functions defined herein.

In some embodiments, the system includes a mobile application, which maybe used to indicate which equipment is operating, or to store data aboutsensor position and condition (for example, photographs). In otherembodiments, the system includes devices for transmitting data to acentral database, performing analysis at this central database andtransmitting results back to the field. The analysis may include methodsfor comparing a particular building's signature and equipment data withthose of other buildings, to build an understanding of likely failuremechanisms and signatures.

FIG. 4 illustrates a flowchart 400 depicting the operation of a sensorin an acoustic sensing system, in one embodiment. Although only onesensor is described, it shall be understood that the sensor may be oneof a plurality of sensors coupled to a pipe system. A sensing device isfirst provided, where the sensor begins in an initial state 410. Thesensing device includes an acoustic sensor (e.g., a microphone oraccelerometer), an analog to digital convertor, and a localmicrocontroller. Sensor state settings may include microcontrollermodes, acoustic frequency range(s), sampling rate, amplifier gain, ADCdynamic range, wake time, wake trigger threshold(s) or other settings.In step 420, the sensor reports to a controller for the system. That is,the data is collected from the sensing devices in the system.Information reported from the sensor may include microphone oraccelerometer data (processed or unprocessed), battery condition,wireless link condition, alarms, temperature, software revision levelsor other sensor status information. Data 430 may be received by thecontroller, such as external data including elements of weathermeasurements and forecasts, information about the pipe system, andinformation from a flow meter attached to the pipe system.

In step 440, the controller for the system analyzes the data usinglearning algorithms to improve accuracy and completeness of informationabout the distribution of flow rates. For example, the controller may bean analysis system that compares processed acoustic signatures withstored templates and historical data. The controller may also bereferred to as a processing component, centralized processing unit, orcentral controller for the purposes of this disclosure. Acoustic signalsfrom normal or intended fluid flow, such as a toilet flush, can therebybe more effectively differentiated from leaks or other unintended fluidflow. As another example, the analysis system may alter parameters inthe sensing devices according to results of comparisons of processedacoustic signatures with the stored templates and historical data. Asanother example, the system may alter parameters in sensing devicesbased upon acoustic characteristics proximal to the sensors. If thesystem receives a relatively clear signal of fluid flow from one sensor,the system may tune other sensor parameters based on the frequenciesreceived from the first sensor. As another example, the system may alterparameters in the sensing devices based upon analyses of the operationof other similarly instrumented piping systems. The system may, forexample, learn the acoustic signature of certain appliances attached tothe piping system, such as toilets and showers, from other installationswith similar appliances and pipe construction. In some embodiments, thelearning algorithms may operate on inaccurate and incomplete data. Forexample, the data may be missing signals from sensors that were removed,inoperable, not installed or too far from fluid flow events to detect agiven event. Inaccurate data may include cases where an actual leak wasmistakenly classified as a false alarm. The learning algorithms mayemploy, for example, support vector machines that can be robust againstinaccurate and incomplete data. In other embodiments, the learningalgorithms are specifically trained to characteristics of the pipesystem, characteristics such as toilet flushes or shower hot and/or coldwater activation and deactivation. The system may, for example, includea training interface that instructs the user to perform a specificactivity (e.g. turn on the shower hot water). In this manner, sensorsignal signatures can be conclusively associated with specific fluidflow events. The collection and analysis of data in steps 420 and 440may be performed on a computer hardware processor.

Parameters received from the controller in step 450 may include acousticfrequency range(s), sampling rate, amplifier gain, ADC dynamic range,wake time, wake trigger threshold(s) or other settings. After settingsare received from the controller, the sensor is now in a modified state460 in which it has, for example, adjusted its wake time to reduce powerconsumption or has had its sensor parameters altered to be tuned toimprove the overall system performance. The sensor will continue toreport data to the controller, as depicted by step 420. Over time, thisloop may be repeated based on the data from the sensor and additionaldata 430.

Although particular components, such as certain battery andmicrocontroller types, have been described, other equivalent componentsthat meet the desired performance characteristics may be substituted.

While the specification has been described in detail with respect tospecific embodiments of the invention, it will be appreciated that thoseskilled in the art, upon attaining an understanding of the foregoing,may readily conceive of alterations to, variations of, and equivalentsto these embodiments. These and other modifications and variations tothe present invention may be practiced by those of ordinary skill in theart, without departing from the scope of the present invention.Furthermore, those of ordinary skill in the art will appreciate that theforegoing description is by way of example only, and is not intended tolimit the invention. Thus, it is intended that the present subjectmatter covers such modifications and variations.

What is claimed is:
 1. A fluid flow monitoring system comprising: aplurality of sensors, the sensors being acoustically coupled to a pipesystem comprising interconnected fluid-transporting pipes, the sensorscollecting sensor data, wherein each pipe of a first subset of thefluid-transporting pipes of the pipe system has at least one of thesensors acoustically coupled thereon, and each pipe of a second subsetof the fluid-transporting pipes has no sensor acoustically coupledthereon; and an analysis system in communication with the plurality ofsensors acoustically coupled to the pipe system and analyzing the sensordata from the plurality of sensors, wherein the analysis system combinessignals from the plurality of sensors to analyze fluid flow in at leastsome of the fluid-transporting pipes of the second subset having nosensor acoustically coupled thereon.
 2. The system of claim 1 whereinthe analysis system is in communication with the plurality of sensors bya wireless network.
 3. The system of claim 1 wherein the sensors arecoupled non-invasively to the pipe system.
 4. The system of claim 1wherein the signals from the plurality of sensors include an acousticsignature from each sensor, the acoustic signature characteristic of aflow event.
 5. The system of claim 1 wherein the analysis systemanalyzes the sensor data using learning algorithms to improve accuracyand completeness of information.
 6. The system of claim 1 wherein, byanalyzing fluid flow, the analysis system identifies fluid leaks in thepipe system.
 7. A method for analyzing fluid flow, the methodcomprising: providing a plurality of acoustic sensing devices, theacoustic sensing devices having i) a microphone or accelerometer, ii) ananalog to digital converter, and iii) a microcontroller; collecting datafrom the plurality of acoustic sensing devices; and analyzing the datausing learning algorithms, wherein the learning algorithms improveaccuracy and completeness of information about a distribution of flowrates in a pipe system.
 8. The method of claim 7 wherein the learningalgorithms operate on inaccurate and incomplete data.
 9. The method ofclaim 7 wherein the learning algorithms are specifically trained tocharacteristics of the pipe system.
 10. The method of claim 7 wherein:in the providing, the plurality of acoustic sensing devices isacoustically coupled to the pipe system comprising interconnectedfluid-transporting pipes; each pipe of a first subset of thefluid-transporting pipes has at least one of the acoustic sensingdevices acoustically coupled thereon, and each pipe of a second subsetof the fluid-transporting pipes has no acoustic sensing deviceacoustically coupled thereon; and the analyzing comprises analyzingfluid flow in at least some of the fluid-transporting pipes of thesecond subset having no acoustic sensing device acoustically coupledthereon.